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A symmetric multivariate leakage correction for MEG connectomes.

Colclough GL, Brookes MJ, Smith SM, Woolrich MW - Neuroimage (2015)

Bottom Line: In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs).This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs.Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses.

View Article: PubMed Central - PubMed

Affiliation: Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; University of Oxford, Dept. Engineering Sciences, Parks Rd., Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK. Electronic address: giles.colclough@magd.ox.ac.uk.

No MeSH data available.


Related in: MedlinePlus

Reconstruction of a five-dipole network: Comparison of ability to infer a simulated network structure under different corrections for source leakage. Five connected dipoles were simulated in the left and right occipital lobes, the right and left frontal lobes, and the right somatosensory cortex (see Fig. 2), for 15 experiments of duration 10 min, at an SNR of 1.0. Group-level z-statistics for the full and partial correlations between the power time-courses of 38 cortical ROIs (Fig. 2B) are shown, after the application of corrections for source leakage. We combine two metrics into one heat map with full correlations below the diagonal and (unregularised) partial correlations above. We compare uncorrected data (B), data adjusted using the symmetric multivariate correction presented in this paper (C), and data corrected for zero-lag correlations pairwise between ROIs (D). C is the only set of matrices which can be thresholded to achieve perfect reconstruction. The higher arrow highlights an example false positive, and the lower a weak true positive with a neighbouring strong false positive (see text). A symmetrical version of the simulated network matrix is shown (A), indicating the relationships between ROIs which would constitute a perfect network discovery for these (undirected) network analysis methods.
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f0015: Reconstruction of a five-dipole network: Comparison of ability to infer a simulated network structure under different corrections for source leakage. Five connected dipoles were simulated in the left and right occipital lobes, the right and left frontal lobes, and the right somatosensory cortex (see Fig. 2), for 15 experiments of duration 10 min, at an SNR of 1.0. Group-level z-statistics for the full and partial correlations between the power time-courses of 38 cortical ROIs (Fig. 2B) are shown, after the application of corrections for source leakage. We combine two metrics into one heat map with full correlations below the diagonal and (unregularised) partial correlations above. We compare uncorrected data (B), data adjusted using the symmetric multivariate correction presented in this paper (C), and data corrected for zero-lag correlations pairwise between ROIs (D). C is the only set of matrices which can be thresholded to achieve perfect reconstruction. The higher arrow highlights an example false positive, and the lower a weak true positive with a neighbouring strong false positive (see text). A symmetrical version of the simulated network matrix is shown (A), indicating the relationships between ROIs which would constitute a perfect network discovery for these (undirected) network analysis methods.

Mentions: Fig. 3 compares group-level estimates of the networked behaviour of the 38 ROIs in the brain, showing both the correlations and partial correlations between the power envelopes of all ROIs. We compare our proposed symmetric multivariate leakage correction to other forms of analysis: to not using a correction at all, and a pair-wise orthogonalisation procedure of ROI time-courses, an extension of a previously-employedvoxel-wise correction for seed-based correlation analyses (see Section 3). We did not attempt to regularise the partial correlations computed for this pair-wise method, which would require a significant increase in algorithmic and computational complexity; in this figure we display only unconstrained partial correlations to compare like with like (λ = 0).


A symmetric multivariate leakage correction for MEG connectomes.

Colclough GL, Brookes MJ, Smith SM, Woolrich MW - Neuroimage (2015)

Reconstruction of a five-dipole network: Comparison of ability to infer a simulated network structure under different corrections for source leakage. Five connected dipoles were simulated in the left and right occipital lobes, the right and left frontal lobes, and the right somatosensory cortex (see Fig. 2), for 15 experiments of duration 10 min, at an SNR of 1.0. Group-level z-statistics for the full and partial correlations between the power time-courses of 38 cortical ROIs (Fig. 2B) are shown, after the application of corrections for source leakage. We combine two metrics into one heat map with full correlations below the diagonal and (unregularised) partial correlations above. We compare uncorrected data (B), data adjusted using the symmetric multivariate correction presented in this paper (C), and data corrected for zero-lag correlations pairwise between ROIs (D). C is the only set of matrices which can be thresholded to achieve perfect reconstruction. The higher arrow highlights an example false positive, and the lower a weak true positive with a neighbouring strong false positive (see text). A symmetrical version of the simulated network matrix is shown (A), indicating the relationships between ROIs which would constitute a perfect network discovery for these (undirected) network analysis methods.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528074&req=5

f0015: Reconstruction of a five-dipole network: Comparison of ability to infer a simulated network structure under different corrections for source leakage. Five connected dipoles were simulated in the left and right occipital lobes, the right and left frontal lobes, and the right somatosensory cortex (see Fig. 2), for 15 experiments of duration 10 min, at an SNR of 1.0. Group-level z-statistics for the full and partial correlations between the power time-courses of 38 cortical ROIs (Fig. 2B) are shown, after the application of corrections for source leakage. We combine two metrics into one heat map with full correlations below the diagonal and (unregularised) partial correlations above. We compare uncorrected data (B), data adjusted using the symmetric multivariate correction presented in this paper (C), and data corrected for zero-lag correlations pairwise between ROIs (D). C is the only set of matrices which can be thresholded to achieve perfect reconstruction. The higher arrow highlights an example false positive, and the lower a weak true positive with a neighbouring strong false positive (see text). A symmetrical version of the simulated network matrix is shown (A), indicating the relationships between ROIs which would constitute a perfect network discovery for these (undirected) network analysis methods.
Mentions: Fig. 3 compares group-level estimates of the networked behaviour of the 38 ROIs in the brain, showing both the correlations and partial correlations between the power envelopes of all ROIs. We compare our proposed symmetric multivariate leakage correction to other forms of analysis: to not using a correction at all, and a pair-wise orthogonalisation procedure of ROI time-courses, an extension of a previously-employedvoxel-wise correction for seed-based correlation analyses (see Section 3). We did not attempt to regularise the partial correlations computed for this pair-wise method, which would require a significant increase in algorithmic and computational complexity; in this figure we display only unconstrained partial correlations to compare like with like (λ = 0).

Bottom Line: In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs).This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs.Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses.

View Article: PubMed Central - PubMed

Affiliation: Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; University of Oxford, Dept. Engineering Sciences, Parks Rd., Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK. Electronic address: giles.colclough@magd.ox.ac.uk.

No MeSH data available.


Related in: MedlinePlus